AVS with deep learning is viable using Deep Reinforcement Learning techniques [1]}, [2]}, [3]}, where visual neural embeddings are often exploited for the action policy training. Han et al.  [1]} proposed a novel deep Q-network (DQN) where the agent state is given by CNN features describing the current RGB observation and the bounding box of the detected object. However, this work is limited since it assumes that the object has to be detected initially. To address the search task, EAT [2]} performs feature extraction from the current RGB observation, and the candidate target crop generated by a region proposal network (RPN). The features are then fed into the Action Policy network. Twelve scenes from the AVD [6]} are used for the training of EAT.
Similarly, GAPLE [3]} uses deep visual features enriched by 3D information (from depth) for the policy learning.
Although GAPLE claims to be generalized, expensive training is the cost to pay as GAPLE is trained with 100 scenes rendered using a simulator House3D based on the synthetic SUNCG dataset.
In general, RL-based strategies are dependent on the training with a large amount of data in order to encode the environmental modelling and motion policy. Differently, our proposed POMCP-based method makes explicit use of the available scene knowledge and performs efficient planning for the agent's path online without additional offline training.
